@inproceedings{el-sayed-etal-2024-aast,
    title = "{AAST}-{NLP}@{\#}{SMM}4{H}`24: Finetuning Language Models for Exact Age Classification and Effect of Outdoor Spaces on Social Anxiety",
    author = "El-Sayed, Ahmed  and
      Nasr, Omar  and
      Tawfik, Noha",
    editor = "Xu, Dongfang  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.smm4h-1.25/",
    pages = "110--113",
    abstract = "This paper evaluates the performance of {\textquotedblleft}AAST-NLP{\textquotedblright} in the Social Media Mining for Health (SMM4H) Shared Tasks 3 and 6, where more than 20 teams participated in each. We leveraged state-of-the-art transformer-based models, including Mistral, to achieve our results. Our models consistently outperformed both the mean and median scores across the tasks. Specifically, an F1-score of 0.636 was achieved in classifying the impact of outdoor spaces on social anxiety symptoms, while an F1-score of 0.946 was recorded for the classification of self-reported exact ages."
}